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Screen resumes and match candidates with GPT-4o, Google Sheets and email

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Screen resumes and match candidates with GPT-4o, Google Sheets and email preview
Open on n8n.io

1. Workflow Overview

How It Works This workflow automates candidate screening and job matching for recruiters, HR operations teams, and talent acquisition leads. It eliminates the manual effort of parsing resumes, eval...

Best for

  • HR automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.agenttool, @n8n/n8n-nodes-langchain.toolcode, n8n-nodes-base.datatable, n8n-nodes-base.set

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheng Siong Chin.

Original n8n.io source

1.1 Workflow description

Title
Screen resumes and match candidates with GPT-4o, Google Sheets and email
Workflow name
Screen resumes and match candidates with GPT-4o, Google Sheets and email

How It Works

This workflow automates candidate screening and job matching for recruiters, HR operations teams, and talent acquisition leads. It eliminates the manual effort of parsing resumes, evaluating multi-dimensional candidate fit, and routing outcomes based on assessment confidence. Resume and job data are received via a POST webhook and passed directly to the Matching Agent Orchestrator, backed by a matching model and shared memory. The orchestrator coordinates four specialist agents in parallel: a Resume Parser Agent (structured extraction), a Skill Analysis Agent (competency mapping), an Experience Assessment Agent (seniority and relevance scoring), and a Cultural Fit Agent (organisational alignment evaluation). A Validation Logic Tool cross-checks outputs before a Ranking Output Parser produces a structured candidate ranking. Results are then checked against a confidence threshold — low-confidence cases trigger a review alert via email and are stored in Google Sheets for human follow-up, while high-confidence matches are prepared as analysis data, stored in Sheets, and distributed as a ranked report via email.

Setup Steps

  1. Import workflow; configure the POST webhook trigger URL for resume and job data ingestion.
  2. Add AI model credentials to the Matching Agent Orchestrator, Resume Parser Agent, Skill Analysis Agent, Experience Assessment Agent, and Cultural Fit Agent.
  3. Link Google Sheets credentials; set sheet IDs for Low Confidence Cases and Analysis Results tabs.
  4. Connect email credentials to the Send Review Required Alert and Send High Confidence Report nodes.
  5. Set confidence threshold values in the Check Confidence Level node.

Prerequisites

  • OpenAI API key (or compatible LLM)
  • Google Sheets with candidate tracking tabs pre-created
  • Email account credentials (SMTP or Gmail OAuth)

Use Cases

  • Recruiters automating high-volume resume screening against structured job descriptions

Customisation

  • Extend specialist agents with domain-specific scoring rubrics for technical or executive roles

Benefits

  • Four parallel specialist agents evaluate candidates across skills, experience, and cultural fit simultaneously

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Receive Resume & Job Data

Type / Role
n8n-nodes-base.webhook - webhook
Config choices
Version 2.1

Block 2 - Matching Agent (Orchestrator)

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 3.1

Block 3 - Matching Agent Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 4 - Ranking Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 5 - Resume Parser Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 6 - Resume Parser Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 7 - Skill Analysis Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 8 - Skill Analysis Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 9 - Experience Assessment Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 10 - Experience Assessment Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 11 - Cultural Fit Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 12 - Cultural Fit Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 13 - Validation Logic Tool

Type / Role
@n8n/n8n-nodes-langchain.toolCode - toolCode
Config choices
Version 1.3

Block 14 - Store Analysis Results

Type / Role
n8n-nodes-base.dataTable - dataTable
Config choices
Version 1.1

Block 15 - Prepare Analysis Data

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 16 - Check Confidence Level

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.3

Block 17 - Send High Confidence Report

Type / Role
n8n-nodes-base.emailSend - emailSend
Config choices
Version 2.1

Block 18 - Send Review Required Alert

Type / Role
n8n-nodes-base.emailSend - emailSend
Config choices
Version 2.1

Block 19 - Prepare Low Confidence Data

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 20 - Store Low Confidence Cases

Type / Role
n8n-nodes-base.dataTable - dataTable
Config choices
Version 1.1

Block 21 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 22 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 23 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 24 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Showing the first 24 of 27 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Screen resumes and match candidates with GPT-4o, Google Sheets and email
Complexity advanced
Nodes 27
Categories HR, AI Summarization
Author Cheng Siong Chin
Published 29 Mar 2026

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/14442/14442.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Screen resumes and match candidates with GPT-4o, Google Sheets and email do?

How It Works This workflow automates candidate screening and job matching for recruiters, HR operations teams, and talent acquisition leads. It eliminates the manual effort of parsing resumes, eval...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your HR, AI Summarization use case.